Dr Paul Pallath, Vice President, Applied AI Practice, Searce Inc

What challenges do you see for data in the year ahead that will have an impact on your organization and on the industry as a whole? 

Industries around the globe are witnessing a surge in data due to the rapid acceleration of digital transformation and a shift towards more nimble, adaptive and agile business processes. However, as companies realize the importance of data in driving intelligent outcomes through AI-driven solutions, they also face increasing complexities in data collection, curation, refinement and transformation. A comprehensive data strategy and vision is necessary to enable data democratization and AI implementation at scale, including establishing data privacy and compliance by design. 

In recent years, businesses have recognized the critical strategic value of data to stay relevant and succeed in today’s highly competitive market. To gain a competitive edge and foster innovation, companies must leverage the power of data, necessitating a re-evaluation of traditional views of data as an outcome of their operations to that of a strategic asset. 

Effective data ownership and governance frameworks must be established to maximize the value captured from data. Moreover, ensuring responsible use of AI is crucial to avoid ethical and legal issues and protect customer privacy. By doing so, companies can unlock the full potential of their data, drive innovation and gain a competitive advantage. 

Have you set out a vision for data? If so, what is it aiming for and does it embrace the whole organization or just the data function?

To drive growth, innovation and better decision-making, it is critical to establish the data maturity of the organization and outline a vision and mission for data usage. However, typical data architectures that mirror the organization’s structure can create data silos and store inconsistent, outdated data, significantly hindering digital transformation efforts. To prioritize data as a strategic asset that supports the organization’s objectives, we created a data strategy and vision for data accessibility, reliability and relevance to all stakeholders, with a focus on developing data literacy across the organization. This vision enabled alignment of our organization’s resources and efforts towards a common goal and enabled cross-functional collaboration, leading to better decision-making and better business outcomes. 

Data silos make it difficult to access and analyze data across the organization, leading to inconsistencies and redundancies in data that hinder informed decision-making and the ability to respond quickly to changing business environments. Therefore, we prioritized data governance, security and privacy while creating a scalable and flexible data infrastructure that can adapt to changing business requirements. 

Ultimately, I believe that creating a data culture that values data insights is essential for winning the market today. A clear vision for data can help break down silos, enable cross-functional collaboration and ensure that everyone is working towards the same objectives. By prioritizing data as a strategic asset, organizations can drive growth, innovation and better decision-making. 

Have you been able to fix the data foundations of your organization, particularly with regard to data quality?

At Searce, our aim is to assist clients in achieving their desired business outcomes by simplifying various components of technology architecture on the cloud, overhauling their data infrastructure and optimizing their business processes, utilizing artificial intelligence to add intelligence. In doing so, we collaborate with clients to establish data and AI blueprints and implementation to enable data as a strategic asset and emphasize that data quality is an essential aspect of the solution. Prior to joining Searce, during my tenure at Levi Strauss & Company, we established a single source of trusted data in the cloud. We cataloged and implemented technology solutions to track the quality of the data. We automated data quality checks as part of our data engineering process to ensure ongoing data quality and detect anomalies for resolution by our team. It was critical to profile any new data ingested into the data ocean to keep the data assets clean. We collaborated with business stakeholders via a data governance council to define data quality standards and implemented them as part of our monitoring tools. Comprehensive measures involving people, processes and technology are needed to ensure data quality at all times.